Summary The principal goal of this proposal is to increase the accuracy and precision of a low-cost autorefraction device called the QuickSee, in order to improve access to refractive eye care for underserved populations. Poor vision due to a lack of eyeglasses is highly prevalent in low-resource settings throughout the world and significantly reduces quality of life, education, and productivity. The existing QuickSee only extracts the lower- order aberration information contained within a wavefront profile of the eye, to roughly estimate an eyeglass prescription. This proposal will further improve the accuracy of the QuickSee device by exploiting both the lower- and higher-order aberrations contained within the complete wavefront. To realize this goal, we will enroll 300 subjects (600 eyes) in Baltimore, MD, and will obtain subjective refraction and visual acuity (VA) measurements and will use machine learning on this large dataset of wavefront profiles to optimize the wavefront-to-refraction algorithm of the QuickSee device. The main output of this project will be a robust and improved-accuracy next-generation QuickSee device that will increase efficiency of and decrease the training requirements of eye care professionals, and potentially dispense refractive correction that provides similar or better VA than correction from an eye care professional. Successful completion of this work will be an important step towards dramatically improving eyeglass accessibility for health disparity populations in the USA and internationally in low-resource settings. Upon completion of this proposal, we will apply for a Phase II award proposing to work with Wilmer Eye Institute research faculty to assess widespread deployment of the next-generation QuickSee with minimally-trained personnel in order to accurately and reliably provide thousands of pairs of low-cost corrective eyeglasses to underserved communities.